{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "voluntary-signal",
   "metadata": {},
   "source": [
    "## Description:\n",
    "这里是DIN的一个demo， 主要分为数据读取与处理，模型搭建，模型的训练三大模块"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "metric-prediction",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-12T07:51:44.981633Z",
     "start_time": "2021-03-12T07:51:41.567762Z"
    }
   },
   "outputs": [],
   "source": [
    "# python基础包\n",
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "\n",
    "# 特征处理与数据集划分\n",
    "from sklearn.preprocessing import OneHotEncoder, MinMaxScaler, StandardScaler, LabelEncoder\n",
    "from sklearn.model_selection import train_test_split\n",
    "from utils import DenseFeat, SparseFeat, VarLenSparseFeat\n",
    "\n",
    "# 导入模型\n",
    "from DIN import DIN\n",
    "\n",
    "# 模型训练相关\n",
    "import tensorflow as tf\n",
    "from tensorflow.keras.layers import *\n",
    "from tensorflow.keras.callbacks import EarlyStopping, ReduceLROnPlateau\n",
    "from tensorflow.keras.metrics import AUC\n",
    "from tensorflow.keras.losses import binary_crossentropy\n",
    "from tensorflow.keras.optimizers import Adam\n",
    "\n",
    "# 一些相关设置\n",
    "import warnings\n",
    "plt.style.use('fivethirtyeight')\n",
    "warnings.filterwarnings('ignore')"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "better-vector",
   "metadata": {},
   "source": [
    "## 导入数据"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "hundred-rapid",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-12T07:51:46.139538Z",
     "start_time": "2021-03-12T07:51:46.124579Z"
    }
   },
   "outputs": [],
   "source": [
    "\"\"\"读取数据\"\"\"\n",
    "# 读取数据\n",
    "samples_data = pd.read_csv(\"data/movie_sample.txt\", sep=\"\\t\", header = None)\n",
    "samples_data.columns = [\"user_id\", \"gender\", \"age\", \"hist_movie_id\", \"hist_len\", \"movie_id\", \"movie_type_id\", \"label\"]"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "trained-gibson",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-12T07:51:47.342365Z",
     "start_time": "2021-03-12T07:51:47.312403Z"
    }
   },
   "outputs": [],
   "source": [
    "\"\"\"数据集\"\"\"\n",
    "X = samples_data[[\"user_id\", \"gender\", \"age\", \"hist_movie_id\", \"hist_len\", \"movie_id\", \"movie_type_id\"]]\n",
    "y = samples_data[\"label\"]\n",
    "behavior_len = np.array([len([int(i) for i in l.split(',') if int(i) != 0]) for l in X['hist_movie_id']])"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "forward-oriental",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-12T07:51:48.001560Z",
     "start_time": "2021-03-12T07:51:47.926761Z"
    }
   },
   "outputs": [],
   "source": [
    "\"\"\"构建DIN模型的输入格式\"\"\"\n",
    "X_train = {\"user_id\": np.array(X[\"user_id\"]), \\\n",
    "            \"gender\": np.array(X[\"gender\"]), \\\n",
    "            \"age\": np.array(X[\"age\"]), \\\n",
    "            \"hist_movie_id\": np.array([[int(i) for i in l.split(',')] for l in X[\"hist_movie_id\"]]), \\\n",
    "            \"seq_length\": behavior_len, \\\n",
    "            \"hist_len\": np.array(X[\"hist_len\"]), \\\n",
    "            \"movie_id\": np.array(X[\"movie_id\"]), \\\n",
    "            \"movie_type_id\": np.array(X[\"movie_type_id\"])}\n",
    "y_train = np.array(y)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "touched-headquarters",
   "metadata": {},
   "source": [
    "## 模型建立"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "accompanied-camera",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-12T07:51:49.189383Z",
     "start_time": "2021-03-12T07:51:49.178414Z"
    }
   },
   "outputs": [],
   "source": [
    "\"\"\"特征封装\"\"\"\n",
    "feature_columns = [SparseFeat('user_id', max(samples_data[\"user_id\"])+1, embedding_dim=8), \n",
    "                        SparseFeat('gender', max(samples_data[\"gender\"])+1, embedding_dim=8), \n",
    "                        SparseFeat('age', max(samples_data[\"age\"])+1, embedding_dim=8), \n",
    "                        SparseFeat('movie_id', max(samples_data[\"movie_id\"])+1, embedding_dim=8),\n",
    "                        SparseFeat('movie_type_id', max(samples_data[\"movie_type_id\"])+1, embedding_dim=8),\n",
    "                        DenseFeat('hist_len', 1)]\n",
    "\n",
    "feature_columns += [VarLenSparseFeat(SparseFeat('hist_movie_id', \n",
    "                                                vocabulary_size=max(samples_data[\"movie_id\"])+1,\n",
    "                                                embedding_dim=8,\n",
    "                                                embedding_name='item_id'), maxlen=50, length_name='seq_length')]\n",
    "\n",
    "# 行为特征列表，表示的是基础特征\n",
    "behavior_feature_list = ['movie_id']\n",
    "# 行为序列特征\n",
    "behavior_seq_feature_list = ['hist_movie_id']"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "stylish-savannah",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-12T07:51:50.465014Z",
     "start_time": "2021-03-12T07:51:50.455998Z"
    }
   },
   "outputs": [],
   "source": [
    "\"\"\"设置超参数\"\"\"\n",
    "learning_rate = 0.001\n",
    "batch_size = 64\n",
    "epochs = 50"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "suited-application",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-12T07:51:53.051101Z",
     "start_time": "2021-03-12T07:51:51.759512Z"
    },
    "scrolled": true
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Model: \"model\"\n",
      "__________________________________________________________________________________________________\n",
      "Layer (type)                    Output Shape         Param #     Connected to                     \n",
      "==================================================================================================\n",
      "user_id (InputLayer)            [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "gender (InputLayer)             [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "age (InputLayer)                [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "movie_id (InputLayer)           [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "movie_type_id (InputLayer)      [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "emb_user_id (Embedding)         (None, 1, 8)         32          user_id[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "emb_gender (Embedding)          (None, 1, 8)         24          gender[0][0]                     \n",
      "__________________________________________________________________________________________________\n",
      "emb_age (Embedding)             (None, 1, 8)         32          age[0][0]                        \n",
      "__________________________________________________________________________________________________\n",
      "emb_movie_id (Embedding)        (None, 1, 8)         1672        movie_id[0][0]                   \n",
      "                                                                 movie_id[0][0]                   \n",
      "__________________________________________________________________________________________________\n",
      "emb_movie_type_id (Embedding)   (None, 1, 8)         80          movie_type_id[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "hist_movie_id (InputLayer)      [(None, 50)]         0                                            \n",
      "__________________________________________________________________________________________________\n",
      "flatten (Flatten)               (None, 8)            0           emb_user_id[0][0]                \n",
      "__________________________________________________________________________________________________\n",
      "flatten_1 (Flatten)             (None, 8)            0           emb_gender[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "flatten_2 (Flatten)             (None, 8)            0           emb_age[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "flatten_3 (Flatten)             (None, 8)            0           emb_movie_id[0][0]               \n",
      "__________________________________________________________________________________________________\n",
      "flatten_4 (Flatten)             (None, 8)            0           emb_movie_type_id[0][0]          \n",
      "__________________________________________________________________________________________________\n",
      "emb_hist_movie_id (Embedding)   (None, 50, 8)        1680        hist_movie_id[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "seq_length (InputLayer)         [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "hist_len (InputLayer)           [(None, 1)]          0                                            \n",
      "__________________________________________________________________________________________________\n",
      "concatenate (Concatenate)       (None, 40)           0           flatten[0][0]                    \n",
      "                                                                 flatten_1[0][0]                  \n",
      "                                                                 flatten_2[0][0]                  \n",
      "                                                                 flatten_3[0][0]                  \n",
      "                                                                 flatten_4[0][0]                  \n",
      "__________________________________________________________________________________________________\n",
      "attention_pooling_layer (Attent (None, 8)            72065       emb_movie_id[1][0]               \n",
      "                                                                 emb_hist_movie_id[0][0]          \n",
      "                                                                 seq_length[0][0]                 \n",
      "__________________________________________________________________________________________________\n",
      "concatenate_1 (Concatenate)     (None, 49)           0           hist_len[0][0]                   \n",
      "                                                                 concatenate[0][0]                \n",
      "                                                                 attention_pooling_layer[0][0]    \n",
      "__________________________________________________________________________________________________\n",
      "dense_4 (Dense)                 (None, 200)          10200       concatenate_1[0][0]              \n",
      "__________________________________________________________________________________________________\n",
      "dense_5 (Dense)                 (None, 80)           16160       dense_4[0][0]                    \n",
      "__________________________________________________________________________________________________\n",
      "dense_6 (Dense)                 (None, 1)            81          dense_5[0][0]                    \n",
      "==================================================================================================\n",
      "Total params: 102,026\n",
      "Trainable params: 102,026\n",
      "Non-trainable params: 0\n",
      "__________________________________________________________________________________________________\n"
     ]
    }
   ],
   "source": [
    "\"\"\"构建DIN模型\"\"\"\n",
    "model = DIN(feature_columns, behavior_feature_list, behavior_seq_feature_list)\n",
    "model.summary()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "former-identifier",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-12T07:51:54.334626Z",
     "start_time": "2021-03-12T07:51:54.314680Z"
    }
   },
   "outputs": [],
   "source": [
    "\"\"\"模型编译\"\"\"\n",
    "model.compile(loss=binary_crossentropy, optimizer=Adam(learning_rate=learning_rate), metrics=[AUC()])"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "identified-scope",
   "metadata": {},
   "source": [
    "## 模型训练"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "impressive-renewal",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-12T07:52:03.033440Z",
     "start_time": "2021-03-12T07:51:55.482559Z"
    }
   },
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Epoch 1/50\n",
      "18/18 [==============================] - 3s 66ms/step - loss: 0.5408 - auc: 0.5076 - val_loss: 0.4845 - val_auc: 0.5011\n",
      "Epoch 2/50\n",
      "18/18 [==============================] - 0s 24ms/step - loss: 0.5473 - auc: 0.4713 - val_loss: 0.4875 - val_auc: 0.4941\n",
      "Epoch 3/50\n",
      "18/18 [==============================] - 0s 25ms/step - loss: 0.4741 - auc: 0.5068 - val_loss: 0.5530 - val_auc: 0.4034\n",
      "Epoch 4/50\n",
      "18/18 [==============================] - 0s 23ms/step - loss: 0.5030 - auc: 0.5062 - val_loss: 0.5171 - val_auc: 0.3007\n",
      "\n",
      "Epoch 00004: ReduceLROnPlateau reducing learning rate to 1.0000000474974514e-05.\n",
      "Epoch 5/50\n",
      "18/18 [==============================] - 0s 23ms/step - loss: 0.5180 - auc: 0.5905 - val_loss: 0.4858 - val_auc: 0.2823\n",
      "Epoch 6/50\n",
      "18/18 [==============================] - 0s 26ms/step - loss: 0.4529 - auc: 0.6111 - val_loss: 0.4737 - val_auc: 0.3641\n",
      "Epoch 7/50\n",
      "18/18 [==============================] - 0s 25ms/step - loss: 0.4350 - auc: 0.5665 - val_loss: 0.4730 - val_auc: 0.3676\n",
      "Epoch 8/50\n",
      "18/18 [==============================] - 0s 25ms/step - loss: 0.4347 - auc: 0.5508 - val_loss: 0.4739 - val_auc: 0.3594\n",
      "Epoch 9/50\n",
      "18/18 [==============================] - 0s 22ms/step - loss: 0.4520 - auc: 0.5907 - val_loss: 0.4740 - val_auc: 0.3578\n",
      "Epoch 10/50\n",
      "18/18 [==============================] - 0s 23ms/step - loss: 0.4390 - auc: 0.5338 - val_loss: 0.4740 - val_auc: 0.3558\n",
      "\n",
      "Epoch 00010: ReduceLROnPlateau reducing learning rate to 1.0000000656873453e-07.\n",
      "Epoch 11/50\n",
      "18/18 [==============================] - 0s 26ms/step - loss: 0.4265 - auc: 0.6316 - val_loss: 0.4740 - val_auc: 0.3558\n",
      "Epoch 12/50\n",
      "18/18 [==============================] - 0s 25ms/step - loss: 0.4275 - auc: 0.6015 - val_loss: 0.4740 - val_auc: 0.3558\n"
     ]
    }
   ],
   "source": [
    "\"\"\"模型训练\"\"\"\n",
    "callbacks = [\n",
    "    EarlyStopping(monitor='val_loss', patience=5, restore_best_weights=True),   # 早停\n",
    "    ReduceLROnPlateau(monitor='val_loss', patience=3, factor=0.01, verbose=1)  # 调整学习率\n",
    "]\n",
    "history = model.fit(X_train, \n",
    "                    y_train, \n",
    "                    epochs=epochs, \n",
    "                    validation_split=0.2, \n",
    "                    batch_size=batch_size,\n",
    "                    callbacks = callbacks\n",
    "                   )"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "convenient-renaissance",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-12T07:52:09.578868Z",
     "start_time": "2021-03-12T07:52:09.415307Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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\n",
      "text/plain": [
       "<Figure size 432x288 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "## \"\"\"可视化下看看训练情况\"\"\"\n",
    "plt.plot(history.history['loss'])\n",
    "plt.plot(history.history['val_loss'])\n",
    "plt.title('Model loss')\n",
    "plt.ylabel('Loss')\n",
    "plt.xlabel('Epoch')\n",
    "plt.legend(['Train', 'Test'], loc='upper left')\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "mediterranean-winning",
   "metadata": {
    "run_control": {
     "marked": true
    }
   },
   "source": [
    "## 模型架构可视化"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "worldwide-chassis",
   "metadata": {
    "ExecuteTime": {
     "end_time": "2021-03-12T07:52:30.246103Z",
     "start_time": "2021-03-12T07:52:29.913946Z"
    }
   },
   "outputs": [
    {
     "data": {
      "image/png": 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      "text/plain": [
       "<IPython.core.display.Image object>"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "from tensorflow import keras\n",
    "keras.utils.plot_model(model, to_file='./DIN_arc.png', show_shapes=True)"
   ]
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3",
   "language": "python",
   "name": "python3"
  },
  "toc": {
   "base_numbering": 1,
   "nav_menu": {},
   "number_sections": true,
   "sideBar": true,
   "skip_h1_title": false,
   "title_cell": "Table of Contents",
   "title_sidebar": "Contents",
   "toc_cell": false,
   "toc_position": {},
   "toc_section_display": true,
   "toc_window_display": false
  },
  "varInspector": {
   "cols": {
    "lenName": 16,
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    "lenVar": 40
   },
   "kernels_config": {
    "python": {
     "delete_cmd_postfix": "",
     "delete_cmd_prefix": "del ",
     "library": "var_list.py",
     "varRefreshCmd": "print(var_dic_list())"
    },
    "r": {
     "delete_cmd_postfix": ") ",
     "delete_cmd_prefix": "rm(",
     "library": "var_list.r",
     "varRefreshCmd": "cat(var_dic_list()) "
    }
   },
   "types_to_exclude": [
    "module",
    "function",
    "builtin_function_or_method",
    "instance",
    "_Feature"
   ],
   "window_display": false
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
